



'''simple docstring'''



import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import YolosForObjectDetection, YolosModel
							from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor
class 						lowerCamelCase_				:







						def __init__(	self :   Tuple					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   Optional[Any]=13					,     lowerCAmelCase__ :   Any=[30, 30]					,     lowerCAmelCase__ :   List[Any]=2					,     lowerCAmelCase__ :   str=3					,     lowerCAmelCase__ :   int=True					,     lowerCAmelCase__ :   Optional[int]=True					,     lowerCAmelCase__ :   List[Any]=32					,     lowerCAmelCase__ :   int=5					,     lowerCAmelCase__ :   List[str]=4					,     lowerCAmelCase__ :   str=37					,     lowerCAmelCase__ :   Dict="gelu"					,     lowerCAmelCase__ :   str=0.1					,     lowerCAmelCase__ :   List[Any]=0.1					,     lowerCAmelCase__ :   int=10					,     lowerCAmelCase__ :   int=0.02					,     lowerCAmelCase__ :   List[Any]=3					,     lowerCAmelCase__ :   Dict=None					,     lowerCAmelCase__ :   Any=8					,     lowerCAmelCase__ :   Any=10					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      parent
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      batch_size
													SCREAMING_SNAKE_CASE       :   Tuple         =      image_size
													SCREAMING_SNAKE_CASE       :   List[str]         =      patch_size
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      num_channels
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      is_training
													SCREAMING_SNAKE_CASE       :   List[Any]         =      use_labels
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      hidden_size
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      num_hidden_layers
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      num_attention_heads
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      intermediate_size
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      hidden_act
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      hidden_dropout_prob
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE       :   List[str]         =      type_sequence_label_size
													SCREAMING_SNAKE_CASE       :   str         =      initializer_range
													SCREAMING_SNAKE_CASE       :   Any         =      num_labels
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      scope
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      n_targets
													SCREAMING_SNAKE_CASE       :   Any         =      num_detection_tokens
													# we set the expected sequence length (which is used in several tests)
													# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
													SCREAMING_SNAKE_CASE       :   Tuple         =      (image_size[1] // patch_size) * (image_size[0] // patch_size)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      num_patches + 1 + self.num_detection_tokens







						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[str]         =      floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]					)

													SCREAMING_SNAKE_CASE       :   str         =      None
													if self.use_labels:
																				# labels is a list of Dict (each Dict being the labels for a given example in the batch)
																				SCREAMING_SNAKE_CASE       :   Any         =      []
																				for i in range(self.batch_size					):
																											SCREAMING_SNAKE_CASE       :   Optional[Any]         =      {}
																											SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.randint(
																											    high=self.num_labels					,     size=(self.n_targets,)					,     device=lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   List[str]         =      torch.rand(self.n_targets					,     4					,     device=lowerCAmelCase__					)
																											labels.append(lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.get_config()

													return config, pixel_values, labels







						def        __lowercase     (	self :   Any					):

													"""simple docstring"""
													return YolosConfig(
													    image_size=self.image_size					,     patch_size=self.patch_size					,     num_channels=self.num_channels					,     hidden_size=self.hidden_size					,     num_hidden_layers=self.num_hidden_layers					,     num_attention_heads=self.num_attention_heads					,     intermediate_size=self.intermediate_size					,     hidden_act=self.hidden_act					,     hidden_dropout_prob=self.hidden_dropout_prob					,     attention_probs_dropout_prob=self.attention_probs_dropout_prob					,     is_decoder=lowerCAmelCase__					,     initializer_range=self.initializer_range					,     num_detection_tokens=self.num_detection_tokens					,     num_labels=self.num_labels					,     )







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   Tuple					,     lowerCAmelCase__ :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      YolosModel(config=lowerCAmelCase__					)
													model.to(lowerCAmelCase__					)
													model.eval()
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      model(lowerCAmelCase__					)
													self.parent.assertEqual(
													    result.last_hidden_state.shape					,     (self.batch_size, self.expected_seq_len, self.hidden_size)					)







						def        __lowercase     (	self :   List[Any]					,     lowerCAmelCase__ :   List[Any]					,     lowerCAmelCase__ :   Optional[Any]					,     lowerCAmelCase__ :   int					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[str]         =      YolosForObjectDetection(lowerCAmelCase__					)
													model.to(lowerCAmelCase__					)
													model.eval()

													SCREAMING_SNAKE_CASE       :   List[Any]         =      model(pixel_values=lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      model(lowerCAmelCase__					)

													self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.num_detection_tokens, self.num_labels + 1)					)
													self.parent.assertEqual(result.pred_boxes.shape					,     (self.batch_size, self.num_detection_tokens, 4)					)

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      model(pixel_values=lowerCAmelCase__					,     labels=lowerCAmelCase__					)

													self.parent.assertEqual(result.loss.shape					,     ()					)
													self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.num_detection_tokens, self.num_labels + 1)					)
													self.parent.assertEqual(result.pred_boxes.shape					,     (self.batch_size, self.num_detection_tokens, 4)					)







						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      self.prepare_config_and_inputs()
													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Any         =      config_and_inputs
													SCREAMING_SNAKE_CASE       :   int         =      {'''pixel_values''': pixel_values}
													return config, inputs_dict


@require_torch
class 						lowerCamelCase_				(       snake_case_     ,					snake_case_     ,					unittest.TestCase      ):
						_lowerCAmelCase  : Union[str, Any]						  =					(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
						_lowerCAmelCase  : Tuple						  =					(
						    {'feature-extraction': YolosModel, 'object-detection': YolosForObjectDetection} if is_torch_available() else {}
						)

						_lowerCAmelCase  : Union[str, Any]						  =					False
						_lowerCAmelCase  : Dict						  =					False
						_lowerCAmelCase  : Tuple						  =					False
						_lowerCAmelCase  : int						  =					False







						def        __lowercase     (	self :   List[str]					,     lowerCAmelCase__ :   Union[str, Any]					,     lowerCAmelCase__ :   Dict					,     lowerCAmelCase__ :   Dict=False					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      super()._prepare_for_class(lowerCAmelCase__					,     lowerCAmelCase__					,     return_labels=lowerCAmelCase__					)

													if return_labels:
																				if model_class.__name__ == "YolosForObjectDetection":
																											SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      []
																											for i in range(self.model_tester.batch_size					):
																																		SCREAMING_SNAKE_CASE       :   Tuple         =      {}
																																		SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.ones(
																																		    size=(self.model_tester.n_targets,)					,     device=lowerCAmelCase__					,     dtype=torch.long					)
																																		SCREAMING_SNAKE_CASE       :   str         =      torch.ones(
																																		    self.model_tester.n_targets					,     4					,     device=lowerCAmelCase__					,     dtype=torch.float					)
																																		labels.append(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   Optional[int]         =      labels

													return inputs_dict







						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      YolosModelTester(self					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      ConfigTester(self					,     config_class=lowerCAmelCase__					,     has_text_modality=lowerCAmelCase__					,     hidden_size=37					)







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													self.config_tester.run_common_tests()







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													# YOLOS does not use inputs_embeds
													pass







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   str         =      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				SCREAMING_SNAKE_CASE       :   int         =      model_class(lowerCAmelCase__					)
																				self.assertIsInstance(model.get_input_embeddings()					,     (nn.Module)					)
																				SCREAMING_SNAKE_CASE       :   str         =      model.get_output_embeddings()
																				self.assertTrue(x is None or isinstance(lowerCAmelCase__					,     nn.Linear					)					)







						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      model_class(lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   int         =      inspect.signature(model.forward					)
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      [*signature.parameters.keys()]

																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      ['''pixel_values''']
																				self.assertListEqual(arg_names[:1]					,     lowerCAmelCase__					)







						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowerCAmelCase__					)







						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      True

													# in YOLOS, the seq_len is different
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self.model_tester.expected_seq_len
													for model_class in self.all_model_classes:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      True
																				SCREAMING_SNAKE_CASE       :   List[str]         =      False
																				SCREAMING_SNAKE_CASE       :   Any         =      True
																				SCREAMING_SNAKE_CASE       :   Optional[int]         =      model_class(lowerCAmelCase__					)
																				model.to(lowerCAmelCase__					)
																				model.eval()
																				with torch.no_grad():
																											SCREAMING_SNAKE_CASE       :   str         =      model(**self._prepare_for_class(lowerCAmelCase__					,     lowerCAmelCase__					)					)
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      outputs.attentions
																				self.assertEqual(len(lowerCAmelCase__					)					,     self.model_tester.num_hidden_layers					)

																				# check that output_attentions also work using config
																				del inputs_dict["output_attentions"]
																				SCREAMING_SNAKE_CASE       :   Tuple         =      True
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      model_class(lowerCAmelCase__					)
																				model.to(lowerCAmelCase__					)
																				model.eval()
																				with torch.no_grad():
																											SCREAMING_SNAKE_CASE       :   int         =      model(**self._prepare_for_class(lowerCAmelCase__					,     lowerCAmelCase__					)					)
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      outputs.attentions
																				self.assertEqual(len(lowerCAmelCase__					)					,     self.model_tester.num_hidden_layers					)

																				self.assertListEqual(
																				    list(attentions[0].shape[-3:]					)					,     [self.model_tester.num_attention_heads, seq_len, seq_len]					,     )
																				SCREAMING_SNAKE_CASE       :   Optional[int]         =      len(lowerCAmelCase__					)

																				# Check attention is always last and order is fine
																				SCREAMING_SNAKE_CASE       :   int         =      True
																				SCREAMING_SNAKE_CASE       :   str         =      True
																				SCREAMING_SNAKE_CASE       :   Dict         =      model_class(lowerCAmelCase__					)
																				model.to(lowerCAmelCase__					)
																				model.eval()
																				with torch.no_grad():
																											SCREAMING_SNAKE_CASE       :   Optional[int]         =      model(**self._prepare_for_class(lowerCAmelCase__					,     lowerCAmelCase__					)					)

																				SCREAMING_SNAKE_CASE       :   Tuple         =      1
																				self.assertEqual(out_len + added_hidden_states					,     len(lowerCAmelCase__					)					)

																				SCREAMING_SNAKE_CASE       :   Dict         =      outputs.attentions

																				self.assertEqual(len(lowerCAmelCase__					)					,     self.model_tester.num_hidden_layers					)
																				self.assertListEqual(
																				    list(self_attentions[0].shape[-3:]					)					,     [self.model_tester.num_attention_heads, seq_len, seq_len]					,     )







						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													def check_hidden_states_output(lowerCAmelCase__ :   Optional[Any]					,     lowerCAmelCase__ :   Tuple					,     lowerCAmelCase__ :   Optional[int]					):
																				SCREAMING_SNAKE_CASE       :   Any         =      model_class(lowerCAmelCase__					)
																				model.to(lowerCAmelCase__					)
																				model.eval()

																				with torch.no_grad():
																											SCREAMING_SNAKE_CASE       :   str         =      model(**self._prepare_for_class(lowerCAmelCase__					,     lowerCAmelCase__					)					)

																				SCREAMING_SNAKE_CASE       :   List[Any]         =      outputs.hidden_states

																				SCREAMING_SNAKE_CASE       :   List[str]         =      getattr(
																				    self.model_tester					,     '''expected_num_hidden_layers'''					,     self.model_tester.num_hidden_layers + 1					)
																				self.assertEqual(len(lowerCAmelCase__					)					,     lowerCAmelCase__					)

																				# YOLOS has a different seq_length
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.model_tester.expected_seq_len

																				self.assertListEqual(
																				    list(hidden_states[0].shape[-2:]					)					,     [seq_length, self.model_tester.hidden_size]					,     )

													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      True
																				check_hidden_states_output(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					)

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				SCREAMING_SNAKE_CASE       :   List[str]         =      True

																				check_hidden_states_output(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					)







						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   str         =      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_object_detection(*lowerCAmelCase__					)







						@slow
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				SCREAMING_SNAKE_CASE       :   Tuple         =      YolosModel.from_pretrained(lowerCAmelCase__					)
																				self.assertIsNotNone(lowerCAmelCase__					)


def        UpperCAmelCase (    ):
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
							return image






@require_torch
@require_vision
class 						lowerCamelCase_				(       unittest.TestCase      ):







						@cached_property
						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''					) if is_vision_available() else None







						@slow
						def        __lowercase     (	self :   Any					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''					).to(lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Any         =      self.default_image_processor
													SCREAMING_SNAKE_CASE       :   Dict         =      prepare_img()
													SCREAMING_SNAKE_CASE       :   str         =      image_processor(images=lowerCAmelCase__					,     return_tensors='''pt'''					).to(lowerCAmelCase__					)

													# forward pass
													with torch.no_grad():
																				SCREAMING_SNAKE_CASE       :   Dict         =      model(inputs.pixel_values					)

													# verify outputs
													SCREAMING_SNAKE_CASE       :   Dict         =      torch.Size((1, 1_00, 92)					)
													self.assertEqual(outputs.logits.shape					,     lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   int         =      torch.tensor(
													    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]					,     device=lowerCAmelCase__					,     )
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      torch.tensor(
													    [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]					,     device=lowerCAmelCase__					)
													self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]					,     lowerCAmelCase__					,     atol=1e-4					)					)
													self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]					,     lowerCAmelCase__					,     atol=1e-4					)					)

													# verify postprocessing
													SCREAMING_SNAKE_CASE       :   int         =      image_processor.post_process_object_detection(
													    lowerCAmelCase__					,     threshold=0.3					,     target_sizes=[image.size[::-1]]					)[0]
													SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]					).to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      [75, 75, 17, 63, 17]
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]					).to(lowerCAmelCase__					)

													self.assertEqual(len(results['''scores''']					)					,     5					)
													self.assertTrue(torch.allclose(results['''scores''']					,     lowerCAmelCase__					,     atol=1e-4					)					)
													self.assertSequenceEqual(results['''labels'''].tolist()					,     lowerCAmelCase__					)
													self.assertTrue(torch.allclose(results['''boxes'''][0, :]					,     lowerCAmelCase__					)					)









'''simple docstring'''



import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf
							from tensorflow.python.eager import context
							from tensorflow.python.framework import ops

							from transformers import GradientAccumulator, create_optimizer
@require_tf
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   List[Any]					,     lowerCAmelCase__ :   str					,     lowerCAmelCase__ :   Dict					,     lowerCAmelCase__ :   int					):

													"""simple docstring"""
													self.assertEqual(len(lowerCAmelCase__					)					,     len(lowerCAmelCase__					)					)
													for a, b in zip(lowerCAmelCase__					,     lowerCAmelCase__					):
																				self.assertAlmostEqual(lowerCAmelCase__					,     lowerCAmelCase__					,     delta=lowerCAmelCase__					)







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      GradientAccumulator()
													accumulator([tf.constant([1.0, 2.0]					)]					)
													accumulator([tf.constant([-2.0, 1.0]					)]					)
													accumulator([tf.constant([-1.0, 2.0]					)]					)
													with self.assertRaises(lowerCAmelCase__					):
																				accumulator([tf.constant([1.0, 1.0]					), tf.constant([2.0, 2.0]					)]					)
													self.assertEqual(accumulator.step					,     3					)
													self.assertEqual(len(accumulator.gradients					)					,     1					)
													self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()					,     [-2.0, 5.0]					,     tol=1e-2					)
													accumulator.reset()
													self.assertEqual(accumulator.step					,     0					)
													self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()					,     [0.0, 0.0]					,     tol=1e-2					)







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      None
													ops.enable_eager_execution_internal()
													SCREAMING_SNAKE_CASE       :   List[Any]         =      tf.config.list_physical_devices('''CPU'''					)
													if len(lowerCAmelCase__					) == 1:
																				tf.config.set_logical_device_configuration(
																				    physical_devices[0]					,     [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      tf.config.list_logical_devices(device_type='''CPU'''					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      tf.distribute.MirroredStrategy(devices=devices[:2]					)

													with strategy.scope():
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      GradientAccumulator()
																				SCREAMING_SNAKE_CASE       :   Dict         =      tf.Variable([4.0, 3.0]					)
																				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Optional[int]         =      create_optimizer(5e-5					,     10					,     5					)
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      tf.Variable([0.0, 0.0]					,     trainable=lowerCAmelCase__					)

													def accumulate_on_replica(lowerCAmelCase__ :   List[str]					):
																				accumulator([gradient]					)

													def apply_on_replica():
																				optimizer.apply_gradients(list(zip(accumulator.gradients					,     [variable]					)					)					)

													@tf.function
													def accumulate(lowerCAmelCase__ :   str					,     lowerCAmelCase__ :   List[Any]					):
																				with strategy.scope():
																											SCREAMING_SNAKE_CASE       :   Optional[Any]         =      strategy.experimental_local_results(lowerCAmelCase__					)
																											local_variables[0].assign(lowerCAmelCase__					)
																											local_variables[1].assign(lowerCAmelCase__					)
																											strategy.run(lowerCAmelCase__					,     args=(gradient_placeholder,)					)

													@tf.function
													def apply_grad():
																				with strategy.scope():
																											strategy.run(lowerCAmelCase__					)

													def _check_local_values(lowerCAmelCase__ :   Dict					,     lowerCAmelCase__ :   int					):
																				SCREAMING_SNAKE_CASE       :   Tuple         =      strategy.experimental_local_results(accumulator._gradients[0]					)
																				self.assertListAlmostEqual(values[0].value()					,     lowerCAmelCase__					,     tol=1e-2					)
																				self.assertListAlmostEqual(values[1].value()					,     lowerCAmelCase__					,     tol=1e-2					)

													accumulate([1.0, 2.0]					,     [-1.0, 1.0]					)
													accumulate([3.0, -1.0]					,     [-1.0, -1.0]					)
													accumulate([-2.0, 2.0]					,     [3.0, -2.0]					)
													self.assertEqual(accumulator.step					,     3					)
													_check_local_values([2.0, 3.0]					,     [1.0, -2.0]					)
													apply_grad()
													self.assertListAlmostEqual(variable.value()					,     [4.0, 3.0]					,     tol=1e-2					)
													accumulator.reset()
													self.assertEqual(accumulator.step					,     0					)
													_check_local_values([0.0, 0.0]					,     [0.0, 0.0]					)




